Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Alexandra Wahab"'
Publikováno v:
Journal of Chemical Information and Modeling. 62:3704-3713
Chemical databases are an essential tool for data-driven investigation of structure-property relationships and design of novel functional compounds. We introduce the first phase of the COMPAS Project – a COMputational database of Polycyclic Aromati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4666610640b84dcc41916b62e4434e3b
https://doi.org/10.1021/acs.jcim.2c00503
https://doi.org/10.1021/acs.jcim.2c00503
Publikováno v:
The Journal of Organic Chemistry.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cc2a7e3fcdc827a850aba3c5fa04f0f
https://doi.org/10.1021/acs.joc.2c02381
https://doi.org/10.1021/acs.joc.2c02381
Publikováno v:
Journal of Physical Organic Chemistry. 36
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6e9f5ee35bcc75b5d5ee0c11c62b431
https://doi.org/10.1002/poc.4458
https://doi.org/10.1002/poc.4458
Publikováno v:
Journal of chemical information and modeling. 62(16)
Chemical databases are an essential tool for data-driven investigation of structure-property relationships and for the design of novel functional compounds. We introduce the first phase of the COMPAS Project─a COMputational database of Polycyclic A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::2476cfa4d8266afe22f5938d1e7fcd7d
https://pubmed.ncbi.nlm.nih.gov/35881922
https://pubmed.ncbi.nlm.nih.gov/35881922
In this work, interpretable deep learning was used to identify structure-property relationships governing the HOMO-LUMO gap and relative stability of polybenzenoid hydrocarbons (PBHs). To this end, a ring-based graph representation was used. In addit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82c1c2e7699f1bf988944a4b4cec604a
https://doi.org/10.26434/chemrxiv-2022-krng1
https://doi.org/10.26434/chemrxiv-2022-krng1
The structure-property relationships of polybenzenoid hydrocarbons (PBHs) were investigated with interpretable machine learning, for which two new tools were developed and applied. First, a novel textual molecular representation, based on the annulat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cf2a4cc543ea1c27980fab19c5809ef
https://doi.org/10.26434/chemrxiv-2022-6dd6n
https://doi.org/10.26434/chemrxiv-2022-6dd6n
Publikováno v:
Electronic Structure, 2 (4)
Polycyclic aromatic systems are prevalent in chemistry and materials science because their thermodynamic stability, planarity, and tunable electronic properties make them uniquely suited for various uses. These properties are closely linked to the ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e2b9ce44bec431bd82b5dee808ca941